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<p><img src="https://pic.rmb.bdstatic.com/bjh/0b1bb7a089cd4a90b4b210dfdcc41a74.png" alt="Snipaste_2021-03-04_11-45-39.png"></p>
<p>这是一篇发表在《Communications of the ACM》2021年三月刊的文章</p>
<p>由于个人兴趣+顺路完成作业，在这里做一篇概要翻译</p>
<p>“计算机的崛起部分是因为技术上的成功，部分也是有资本力量推动。计算机之类的通用技术产品具有广泛的技术适用性，产品的改进和市场的增长能在数十年内互相促进。但到了它生命周期的末端：随着技术进步放缓，其它产品可能会在部分领域替代这种通用产品，破坏经济上的自我强化循环。今天我们正目睹计算机的这种改变：<strong>随着 CPU 改进的放缓，应用转向了专门处理器</strong>，如 GPU 虽然做的任务比 CPU 少，但在特定任务上性能更出色。深度学习和比特币挖矿等应用也都转向了专门处理器。计算机作为一种通用技术开始衰落了”——摘自Solidot</p>
<p>DOI：<a target="_blank" rel="noopener external nofollow noreferrer" href="http://dx.doi.org/10.1145/3430936">http://dx.doi.org/10.1145/3430936</a></p>
<p>作者：Neil C. Thompson是美国麻省理工学院计算机科学与人工智能实验室和数字化经济项目的创新学者。Svenja Spanuth是瑞士苏黎世联邦理工学院的管理、技术和经济系的博士研究生</p>
<h2 id="通用（Universal）计算起源与发展"><a href="#通用（Universal）计算起源与发展" class="headerlink" title="通用（Universal）计算起源与发展"></a>通用（Universal）计算起源与发展</h2><p>首先要明白的是，所谓的第一台电脑ENICA，只能进行单一计算（计算火炮射程表），计算内容稍微做出一些改变，里面的电子元器件就需要重新设计</p>
<p><img src="https://pic.rmb.bdstatic.com/bjh/6daf2a32cac2df90dff4f00e13726ace.jpeg"></p>
<p>人们想到的办法就是：<strong>让计算机存储指令</strong>，通过不同指令实现不同的活动</p>
<p>就这样，冯·诺伊曼架构的机器出现了，电脑从而达到了GPT（General Purpose Technology）（可以理解为“通用目的机器”）。计算机科学随之发展，并在上世纪70年代进入了发展快车道。在上个世纪的最后一个十年，因为科学技术领域方面的需要，Intel总共花了<strong>1830亿</strong>(美元)用于建造厂房生产芯片，而计算机运行速度相比较于1971年增长了<strong>400 000倍</strong>，在摩尔定律的预言下，硬件愈发便宜，性能愈加强大。自1974年以来，美国的所有生产力提升中三分之一归功于信息技术（IT），成为促进国家繁荣发展的最大功臣之一</p>
<p>通用计算进入市场的时候，实现的是一个良性的循环（技术进步—更多用户使用—获得经济利益—技术继续进步）</p>
<p><img src="https://pic.rmb.bdstatic.com/bjh/447e961fcb6dcd4d20549297a6e2f303.png" alt="image-20210303214156373.png"></p>
<h2 id="专用（Pratical）计算发展"><a href="#专用（Pratical）计算发展" class="headerlink" title="专用（Pratical）计算发展"></a>专用（Pratical）计算发展</h2><p>通用处理器必须能够很好地执行许多不同的计算。这导致了使许多计算快速完成的设计折衷方案，<strong>但没有一个是最优方案</strong></p>
<p>在通用计算大规模发展的同时，专用计算也在发展，最为典型的就是GPU（这个概念最早是英伟达的黄仁勋提出来的）玩家对游戏性能的需要促进了显卡的发展，在这一点上，CPU上的显示功能远远无法满足玩家的需求（Tips：感兴趣的话，可以看一下B站极客湾有关显卡的视频）</p>
<p><a target="_blank" rel="noopener external nofollow noreferrer" href="https://www.bilibili.com/video/av47052564">-&gt;传送门</a></p>
<p>报告以2017年的Nvidia P100与Intel Xeon  E5-2690v4做对比，在<strong>运算速度</strong>上CPU远远超过了GPU，但在<strong>并行计算</strong>，<strong>内存带宽</strong>，以及<strong>一级缓存</strong>的容量上面CPU远落后于GPU。</p>
<p><img src="https://pic.rmb.bdstatic.com/bjh/2b7b2f11ac0da1f4dddb02b411b6f340.png" alt="image-20210303212825101.png"></p>
<p>而在同样的运算量上，GPU消耗的能源要比CPU要少，在深度学习训练的模型当中，大内存带宽使得GPU能更快训练出模型（相比较于CPU）</p>
<p>在超级计算机方面，2019年前十的超级计算机当中有九台都用了英伟达的GPU</p>
<p>(这个轮式存在问题，前十里面有两台是中国的计算机，分别是神威太湖之光和天河二号，天河二号已知用了intel的技术，太湖之光是全国产机器，但当年第一位的超级计算机summit用了英伟达的技术)</p>
<p>而在物联网，云计算，手机，这些快速发展的产业中间，专用计算因为其能耗较少，被大厂商们所青睐，广泛用于现代生活当中。虽说PC机依然是主流计算平台，但芯片功能专一化这一趋势已经不可阻挡</p>
<h2 id="消逝的光环"><a href="#消逝的光环" class="headerlink" title="消逝的光环"></a>消逝的光环</h2><p>首先，通用计算发展迟缓很重要的一个原因，就是<strong>性价比开始降低</strong></p>
<p><img src="https://pic.rmb.bdstatic.com/bjh/39338c216a2200e2438d7ec08d7e8b51.png" alt="image-20210303220208811.png"></p>
<p>摩尔定律随着时间的推演开始被打破，使得通用计算不再具有之前的性价比，使得在经济上对通用计算的投资<strong>转向了专用计算</strong></p>
<p>![image-20210304113449331](D:\PublicPropaganda\公众号\报告翻译：The Decline of Computers as a General Purpose Technology.assets\image-20210304113449331.png)</p>
<p>原本的良性经济循环开始解体，变为了恶性循环</p>
<p>文章援引英特尔CEO Krzanich（这个人在2018年离开CEO职位）在2016年的话说：PC机的更换周期将由四年变为5到6年，用户甚至可能会跳过多代产品去选择那些值得更新的（Worth Updating）</p>
<p>文章中给了一个公式，用来判断专用计算是否比通用计算更有吸引力</p>
<p><img src="https://pic.rmb.bdstatic.com/bjh/c45c8acf4a28e3da9bad2d23213b8dd5.png" alt="image-20210304115926719.png"></p>
<p>（P代表性能（performance），r代表通用计算性能的改善，T和t代表时间）</p>
<p>再仔细深究下去，可以发现，随着工艺制程不断进步，有能力生产先进制程的厂商越来越少，逐渐呈现出了垄断的趋势</p>
<p><img src="https://pic.rmb.bdstatic.com/bjh/3dca2565880a4a71a0e5f0b8fb3d1a49.png" alt="image-20210303222204879.png"></p>
<p>就算抛开垄断因素不谈（无端的指责是没有意义的），工艺开发成本以每年13%的速度上涨，和生产芯片的流片（Fab）成本也在逐年上涨</p>
<p><img src="https://pic.rmb.bdstatic.com/bjh/eb9a16725160bc751153a6a638b3b423.png" alt="image-20210304124831131.png"></p>
<p>与此同时，华为，阿里巴巴，亚马逊，谷歌，百度等厂商也开始自研芯片，专用芯片相较于通用芯片而言更好研发，且能很快获得经济效益（亚马逊，阿里巴巴，谷歌都有自己的云计算，云计算中的<strong>裸金属服务器</strong>就是靠专用芯片实现的）</p>
<p>（Tips：感兴趣的可以看浅黑科技的文章  阿里造“神龙” ）</p>
<p><a target="_blank" rel="noopener external nofollow noreferrer" href="https://mp.weixin.qq.com/s/Jp0CeqxyB6hlIeGyiw6Gnw">-&gt;传送门</a></p>
<h2 id="结语部分"><a href="#结语部分" class="headerlink" title="结语部分"></a>结语部分</h2><p>报告在这里进行了展望，那些需要<strong>大规模交换数据与大量运算</strong>的业务，会优先用上专用芯片</p>
<p>而那些效益没有那么高的，市场规模不大，或者是性能要求不高的（文章中举的例子是数据库存储），则不会那么快用上专业芯片</p>
<p>还有就是小厂商，他们因为没有能力与财力设计，找代工厂生产芯片，会继续使用CPU进行运算</p>
<p>是否会有技术改进恢复通用处理器改进的步伐？业界热议的<strong>量子计算</strong>，<strong>碳纳米管</strong>和<strong>光学计算机</strong>有可能成为这种技术，但是专家们预计再过十年，业界才能设计出这样的计算机（例如，可以替代通用传统计算机的量子计算机），但这需要大量的金钱与人力投入成本，短期之内无法见到效益。</p>
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